Most AI Agents Are Useless. Let’s Fix That

PyData Berlin 2025 - https://cfp.pydata.org/berlin2025/talk/CAUAZY/

AI agents are having a moment, but most of them are little more than fragile prototypes that break under pressure. Together, we’ll explore why so many agentic systems fail in practice, and how to fix that with real engineering principles. In this talk, you’ll learn how to build agents that are modular, observable, and ready for production. If you’re tired of LLM demos that don’t deliver, this talk is your blueprint for building agents that actually work.

Let’s face it: most AI agents are glorified demos. They look flashy, but they’re brittle, hard to debug, and rarely make it into real products. Why? Because wiring an LLM to a few tools is easy. Engineering a robust, testable, and scalable system is hard.

This talk is for practitioners, data scientists, AI engineers, and developers who want to stop tinkering and start shipping. We’ll take a candid look at the common reasons agent systems fail and introduce practical patterns to fix them using Haystack, an open-source Python framework to build custom AI applications.

You’ll learn how to design agents that are:

Modular, so they’re easy to extend and evolve
Observable, so you can trace failures and understand the behavior
Maintainable, so they don’t become one-off science projects

Whether you’re just starting to explore agents or trying to tame an unruly prototype, you’ll leave with a clear, actionable blueprint to build something that’s not just smart, but also reliable.

Transcript

1. Most AI Agents Are Useless. Letʼs Fix That Bilge Yücel, Developer Relations Engineer @ deepset
2. Bilge Yucel Hello 👋 • Developer Relations Engineer at deepset • Istanbul → Berlin in/bilge-yucel @bilgeyucl
3. Our Context Company Solving Custom AI challenges since 2018. HQ in Berlin and NYC. Backed by: Leading open source framework & commercial platforms for custom enterprise-grade AI Products Used by 70 Thought leaders
4. Where are you on your agent journey?
5. What is an AI agent? An AI agent is an LLM-based system that autonomously pursues a goal by interacting with its environment using tools. Human LLM Call Environment Action Feedback Stop
6. Why Agents Fail? 🤔
7. “Agent involves a lot more work than expected and 90% of it is pure engineering. Nothing really to do with LLM, but it is how to blend LLM into an agentic workflow that makes senseˮ - a Haystack community member
8. Failure Reason 1 Too Much Trust, Too Little Design ❌ Brittle prompts ❌ Too many tools ❌ No fallback plans ❌ No observability ❌ No guardrails
9. Failure Reason 2 Wrong Use Case • You have complex, multi-step problems requiring diverse actions • Tasks involve multiple tools / sources • Goals are clear but the optimal path to achieve them isn't predetermined
10. Failure Reason 2 Wrong Use Case • You have complex, multi-step problems requiring diverse actions • Tasks involve multiple tools / sources • Goals are clear but the optimal path to achieve them isn't predetermined • When interactions can follow predictable patterns (e.g. Q&A • Tasks can be decomposed into clear steps • Stability, robustness and efficiency are prioritized over automation potential
11. Failure Reason 2 Wrong Use Case • You have complex, multi-step problems requiring diverse actions • Tasks involve multiple tools / sources • Goals are clear but the optimal path to achieve them isn't predetermined • When interactions can follow predictable patterns (e.g. Q&A • Tasks can be decomposed into clear steps • Stability, robustness and efficiency are prioritized over automation potential Agents for Reasoning, Pipelines for Defined Flow
12. AI Workflows & Agents Itʼs a spectrum Operational Risk Autonomy AI Agent Deterministic AI Workflow Variables, outputs, com plexity • Higher autonomy ◦ Complex problems ✅ ◦ Higher risks ❌ • Blend the two • Balance of Performance vs. Speed vs. Costs
13. How to design reliable agents?
14. • Open-source AI orchestration framework by deepset • Backbone of the deepset AI Platform • Provides the tools that Python developers need to build real world, agentic AI systems with visibility, control and modularity • Building blocks: Components & Pipelines
15. Haystack Agents User Request Agent LLM (e.g. OpenAI, Anthropic, Google, OS System Prompt Python Functions External APIs Haystack Components MCP Servers Generated Answer
16. Optimal Level of Autonomy Github Issue Resolver Agent Build a GitHub Issue Resolver Agent
17. https://github.com/bilgeyucel/haystack-personal-fork/issues/18
18. Split the Task Multi-agent systems Build a GitHub PR Creator Agent
19. Observability • Gain insights on whatʼs happening inside your agentic system • Debugging & Hallucination detection
20. Demo
21. https://itinerary-agent.deepset.ai
22. Practical Tips For Building Agents • Find the optimal level of autonomy. Have the control over your system. • Minimize tools. Each new tool adds failure risk. • Split agents/multi-agent. Donʼt make one agent do everything. Use planners, coders, fixers. • Build ‘fatʼ tools. Encapsulate logic, retries, and fallback paths into the tool logic. • Build guardrails. Validate inputs, actions and outputs. • Log everything. Add tracing to every step, tool, and output. • Human-in-the-loop. Let people oversee and guide agents.
23. Unbreakable AI Agents with Haystack Build a Tool-Calling Agent AI Guardrails: Content Moderation and Safety with Open Language Models Creating a Multi-Agent System with Haystack Trace and Evaluate RAG with Arize Phoenix DevOps Support Agent with Human in the Loop Build a GitHub Issue Resolver Agent Build a GitHub PR Creator Agent Building AI Agents with Haystack
24. Thank you www.deepset.ai www.haystack.deepset.ai https://itinerary-agent.deepset.ai in/bilge-yucel @bilgeyucl